MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations

Xiang Lin; Yunying Li; Chengzhi Ye; Zitong Chen; Jing Sun

cs.LGAug 18, 2026

MAGPIE-Net: Predicting short-duration heavy-rainfall events in station neighborhoods from multitemporal FY-4A AGRI observations

Authors: Xiang Lin, Yunying Li, Chengzhi Ye, Zitong Chen, Jing Sun

Organizations: College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China · Key Laboratory of High Impact Weather (Special), China Meteorological Administration, Changsha 410073, China · Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction, Changsha 410118, China · Institute of Meteorological Science of Hunan Province, Changsha 410118, China

Abstract

Short-duration heavy-rainfall warning determines whether 1 h rainfall will exceed a threshold within a target-station neighborhood over the next few hours. Multitemporal infrared and water-vapor observations from the Fengyun-4A Advanced Geostationary Radiation Imager (FY-4A AGRI) capture cloud-top cooling, moisture evolution, and cloud expansion before substantial surface rainfall develops. However, most deep-learning nowcasting methods convert these signals into local warnings by post-processing gridded precipitation predictions, preventing station-neighborhood event targets from directly supervising the satellite-to-station learning pathway. We propose MAGPIE-Net, which embeds a geographically adaptive, differentiable grid-to-station mapping in a pathway combining convection-initiation features, multiscale encoding, and auxiliary gridded precipitation diagnosis. Station-neighborhood event losses thereby constrain the satellite representation and its mapping to irregular station locations for 0-3 h event prediction. In independent 2023 warm-season tests over central and eastern China, critical success index (CSI) values under the primary 40 km/20 mm h-1 definition were 0.371, 0.304, and 0.238 at 0-1, 1-2, and 2-3 h. Across episodes, MAGPIE-Net achieved a detection rate of 65.1% and a mean lead time of 64.6 min, compared with 23.6% and 18.3 min for the best gridded-output baseline, and remained superior for smaller neighborhoods and the 50 mm h-1 threshold. During the critical early-warning stage, when antecedent 1 h rainfall within 40 km remained below 1 mm, MAGPIE-Net detected 51.9% of episodes with a mean lead time of 38.5 min. These results show that event-oriented satellite-to-station modeling converts multitemporal geostationary cloud and moisture observations into local heavy-rainfall warnings more effectively than gridded-precipitation modeling.

Figures & tables

Figure 1: Comparison of the conventional gridded-output pathway and the event-oriented satellite-to-station modeling pathway for satellite-based short-duration heavy-rainfall warning. Both pathways use regional multichannel geostationary satellite sequences. In the conventional pathway, model training and verification are based on gridded precipitation, and station-neighborhood events are subsequently derived through radius-based neighborhood aggregation. The event-oriented pathway incorporates a learnable grid-to-station mapping and uses station-neighborhood heavy-rainfall events as the common target for training, verification, and warning output.

Figure 2: Study domain and station networks. (a) Distribution of national meteorological stations, with marginal counts by longitude and latitude. (b) Location of the study domain within China. (c) Gaussian-smoothed relative density of regional automatic weather stations in the precipitation dataset.

Channel	Type	Central wavelength	Native spatial resolution
7	Mid-wave infrared (high gain)	$3.75\,\mu\mathrm{m}$	2 km a
8	Mid-wave infrared (low gain)	$3.75\,\mu\mathrm{m}$	4 km
9	Upper-tropospheric water vapor	$6.25\,\mu\mathrm{m}$	4 km
10	Mid-tropospheric water vapor	$7.10\,\mu\mathrm{m}$	4 km
11	Long-wave infrared	$8.50\,\mu\mathrm{m}$	4 km
12	Thermal-infrared window	$10.70\,\mu\mathrm{m}$	4 km

Table 1: FY-4A AGRI channels used as model input.

Figure 3: Overall architecture of MAGPIE-Net. The input AGRI sequence is used to construct CI features, which are combined with the original AGRI channels by the multiscale satellite encoder. The encoder produces a regional satellite representation, from which the auxiliary diagnosis head generates three gridded precipitation fields. GA-SetConv maps these fields to target stations, and the multi-head module predicts event probabilities for three forecast windows. CBR denotes a convolution–batch-normalization–ReLU block. For clarity, repeated indices are omitted from the GA-SetConv inset; the complete notation is defined in Section 3.4 .

Item	Specification or condition
Candidate times	$\tau=t_{0}-30\,\mathrm{min}$ and $t_{0}-15\,\mathrm{min}$ , each evaluated using the three scans at $\tau-30$ , $\tau-15$ , and $\tau$
Motion consistency	Backward trajectories remain within the grid, displacement in each 15-min step is at most 12 pixels, and forward–backward error is at most 1.5 pixels
Trajectory notation	$\widetilde{T}_{10.7}$ denotes a temperature sampled along the backward-traced cloud trajectory
Cold cloud top	$T_{10.7}(\tau,g)<273\,\mathrm{K}$
Continuous cooling	$\widetilde{T}_{10.7}(\tau-15,g)-\widetilde{T}_{10.7}(\tau-30,g)\leq-4\,\mathrm{K}$ and $T_{10.7}(\tau,g)-\widetilde{T}_{10.7}(\tau-15,g)\leq-4\,\mathrm{K}$
Water-vapor–infrared difference	$T_{7.1}(\tau,g)-T_{10.7}(\tau,g)>-30\,\mathrm{K}$

Table 2: Parameters and criteria for constructing CI candidate masks.

Figure 4: Station-neighborhood warning performance under the primary $40\,\mathrm{km}/20\,\mathrm{mm\,h^{-1}}$ event definition. Gridded-output predictions are converted into station-neighborhood events using the same radius-based neighborhood aggregation. (a)–(c) Sample-level CSI, POD, and FAR for the three forecast windows. (d) Episode detection rate. (e) Mean episode warning lead time, with undetected episodes assigned 0 min.

Figure 5: Episode-level warning performance stratified by antecedent rainfall within the 40-km neighborhood under the primary event definition.

P_{\mathrm{ante}}

denotes the maximum 1 h accumulated rainfall during the non-overlapping hour immediately preceding the first threshold-exceeding window. (a) Episode detection rate. (b) Mean episode warning lead time, with undetected episodes assigned 0 min. Numbers beneath the categories indicate the observed episode counts.

Model	$40/20$	$20/20$	$10/20$	$40/50$	$20/50$	$10/50$	Mean
Grid-trained backbone	0.0456	0.0291	0.0165	0.0007	0.0004	0.0002	0.0154
Bilinear event model	0.2873	0.1802	0.1086	0.1226	0.0703	0.0294	0.1331
MAGPIE-Net	0.3043	0.1909	0.1183	0.1315	0.0772	0.0376	0.1433

Table 3: Controlled comparison of the event-oriented pathway and grid-to-station decoding. Values are CSI averaged over the three forecast windows.

Figure 6: Cross-model CSI across neighborhood radii and rainfall thresholds. Gridded-output predictions are evaluated using the same radius-based neighborhood aggregation as the station-neighborhood event definition. Panels (a)–(c) correspond to

20\,\mathrm{mm\,h^{-1}}

, and panels (d)–(f) correspond to

50\,\mathrm{mm\,h^{-1}}

. Columns represent neighborhood radii of 40, 20, and 10 km. Each panel shows the 0–1, 1–2, and 2–3 h forecast windows.

Figure 7: Performance of MAGPIE-Net across temporal and regional subsets under the primary event definition. (a) Sample-level CSI, POD, FAR, and positive-event counts for daytime and nighttime samples. (b) Monthly sample-level metrics and positive-event counts for the three forecast windows, together with episode detection rate and mean episode warning lead time. (c) Regional partition and sample-level CSI, POD, and FAR across the five regions.

Figure 8: CI-feature ablation and inference-time spatial attribution under the primary event definition. (a)–(c) Detected episode count, episode detection rate, and mean warning lead time for independently trained MAGPIE-Net models without and with CI features. (d)–(f) CI-centered station-neighborhood event-probability profiles obtained with the full input and the CI-masked input. (g)–(i) Event-probability differences between the full and CI-masked inputs; the insets compare the mean differences for positive and negative station–time events.

Figure 9: Station-neighborhood event predictions for a localized convective heavy-rainfall episode over coastal southern Guangxi, initialized at 22:00 UTC on 10 August 2023 (06:00 BJT on 11 August). The top row shows the latest input AGRI $10.7\,\mu\mathrm{m}$ image at 21:45 UTC (05:45 BJT), observed positive events in the three forecast windows, and an enlarged view of the newly positive component during 1–2 h. The rightmost column uses the same AGRI $10.7\,\mu\mathrm{m}$ background for all rows. The remaining rows show predictions from PhyDNet, NPM, NowcastNet, and MAGPIE-Net. EarthFormer, omitted for clarity, also missed all three stations in the highlighted component. Cyan, orange, red, and blue markers denote hits, false alarms, misses, and observed positive events, respectively.

Figure 10: Station-neighborhood event predictions during the landfall of Typhoon Doksuri, initialized at 00:00 UTC (08:00 BJT) on 28 July 2023. The top row shows the latest input AGRI $10.7\,\mu\mathrm{m}$ image, observed positive events in the three forecast windows, and an enlarged coastal view during 2–3 h. The rightmost column uses the same AGRI $10.7\,\mu\mathrm{m}$ background for all rows. The remaining rows show predictions from PhyDNet, NPM, NowcastNet, and MAGPIE-Net. Cyan, orange, red, and blue markers denote hits, false alarms, misses, and observed positive events, respectively.

Precipitation Nowcasting

Precipitation nowcasting predicts rainfall over minutes to hours from radar, satellite, station, and numerical weather data.

See all 40 papers

IMPA-Net: Meteorology-Aware Multi-Scale Attention and Dynamic Loss for Extreme Convective Radar Nowcasting
Apr 27, 2026Haofei Cui, Guangxin He, Juanzhen Sun +5Precipitation Forecasting Multi-Scale Feature Fusion
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME) / International Joint Research Laboratory on Climate and Environment Change (ILCEC) / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China. · Nanchang Meteorological Bureau, Nanchang 330000, China. · National Center for Atmospheric Research, Boulder, CO 80301, USA. +3
A plug-and-play generative framework for multi-satellite precipitation estimation
May 14, 2026Yunfan Yang, Haofei Sun, Xiuyu Sun +7Remote Sensing Generative Modeling
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. · College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China. · Shanghai Academy of Artificial Intelligence for Science (SAIS), Shanghai, China. +5
Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks
Jun 16, 2026Ophélia Miralles, Máté Mile, Christoffer Artturi +2Precipitation Forecasting Probabilistic Forecasting
Norwegian Meteorological Institute, Oslo, Norway

Apr 27, 2026J/K move · Enter open · S save

IMPA-Net: Meteorology-Aware Multi-Scale Attention and Dynamic Loss for Extreme Convective Radar Nowcasting

Haofei Cui, Guangxin He, Juanzhen Sun, Jingjia Luo+4

Key Laboratory of Meteorological Disaster, Ministry of Education (KLME) / International Joint Research Laboratory on Climate and Environment Change (ILCEC) / Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China. · Nanchang Meteorological Bureau, Nanchang 330000, China. · National Center for Atmospheric Research, Boulder, CO 80301, USA.+3

Precipitation Forecasting Multi-Scale Feature Fusion Adaptive Loss Weighting Precipitation Nowcasting Multi-Scale Attention

Open paper AskPDF arXiv

Short-range prediction of convective precipitation from weather radar observations is essential for severe weather warnings. However, deep learning models trained with pixel-wise error metrics tend to produce overly smooth forecasts that suppress intense echoes critical for hazard detection. This issue is exacerbated by insufficient multi-scale feature interaction and suboptimal fusion of heterogeneous geophysical inputs. We propose IMPA-Net (Integrated Multi-scale Predictive Attention Network), a deterministic 0-2 hour nowcasting framework that addresses these limitations through meteorologically-informed designs at the input, architecture, and loss function levels. A parameter-free Spatial Mixer reorganizes heterogeneous input channels at the mesoscale-

γ

neighborhood (~2 km) via deterministic channel permutation, providing a structured cross-field prior. An integrated multi-scale predictive attention module serves as the spatiotemporal translator, capturing dynamics from mesoscale-

β

to mesoscale-

γ

scales. A Meteorologically-Aware Dynamic Loss employs three-level asymmetric weighting -- adapting across training epochs, storm intensity, and forecast lead time -- to counteract regression-to-the-mean. Evaluated against seven baselines on a multi-source radar dataset over eastern China, IMPA-Net raises the Heidke Skill Score at

\geq

45 dBZ from 0.049 (SimVP baseline) to 0.143 under matched settings. Relative to pySTEPS, it provides a better trade-off between severe-event detection and false-alarm control. Spectral analysis confirms preserved energy across mesoscale bands where competing methods show progressive smoothing. These improvements are shown within a single domain and convective regime; generalizability to other orographic and climatic regions remains to be tested.

Abstract

Precipitation Nowcasting

Explore similar work

IMPA-Net: Meteorology-Aware Multi-Scale Attention and Dynamic Loss for Extreme Convective Radar Nowcasting

A plug-and-play generative framework for multi-satellite precipitation estimation

Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks